Method of controlling for undesired factors in machine learning models

ABSTRACT

A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analyses of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant&#39;s appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.

RELATED APPLICATIONS SECTION

The present application is a continuation of, and claims the benefit of,U.S. patent application Ser. No. 15/383,659, filed Dec. 19, 2016 andentitled “Method of Controlling for Undesired Factors in MachineLearning Models,” which is related to and claims priority benefit of anearlier-filed provisional patent application having the same title, Ser.No. 62/272,184, filed Dec. 29, 2015, and a second earlier-filedprovisional patent application having the same title, Ser. No.62/273,624, filed Dec. 31, 2015. Further, the present application isrelated to a first co-filed U.S. non-provisional patent applicationhaving the same title, Ser. No. 15/383,499, filed Dec. 19, 2016, and toa second co-filed U.S. patent application having the same title, Ser.No. 15/383,567, filed Dec. 19, 2016. The entire contents of theidentified earlier- and co-filed applications are hereby incorporated byreference into the present application as if fully set forth herein andtheir entireties.

FIELD OF THE INVENTION

The present disclosure generally relates to methods of training andusing machine learning models. More particularly, the present disclosureconcerns a method of training and using a machine learning model thatcontrols for the model's consideration of one or more undesired factorswhich might otherwise be considered by the model in operation.

BACKGROUND

Machine learning models may be trained to analyze information forparticular purposes involving identifying correlations and makingpredictions. During training, the models may learn to includeillegitimate, non-useful, irrelevant, misleading, or otherwise undesiredfactors, especially if such biases are present in the training datasets. In particular, while training with structured data involveslimiting the data that a model considers, training with unstructureddata allows the model to consider all available data, includingbackground information and other undesired factors. For example, aneural network trained with unstructured data including people'sappearances to make correlations and predictions about those people mayconsider such undesired factors as age, sex, ethnicity, and/or race inits subsequent analyses.

BRIEF SUMMARY

Embodiments of the present technology relate to machine learning modelsthat control for consideration of one or more undesired factors whichmight otherwise be considered by the machine learning model whenanalyzing new data. For example, one embodiment of the present inventionmay be configured for training and using a neural network that controlsfor consideration of one or more undesired factors which might otherwisebe considered by the neural network when analyzing new data as part ofan underwriting process to determine an appropriate insurance premium.

In a first aspect, a method of training and using a machine learningmodel that controls for consideration of one or more undesired factorswhich might otherwise be considered by the machine learning model maybroadly comprise the following. The machine learning model may betrained using a training data set that contains information includingthe undesired factors. The undesired factors and one or more relevantinteraction terms between the undesired factors may be identified. Themachine learning model may then be caused to not consider the identifiedundesired factors when analyzing the new data to control for undesiredprejudice or discrimination in machine learning models.

In a second aspect, a computer-implemented method for training and usinga machine learning model to evaluate an insurance applicant as part ofan underwriting process to determine an appropriate insurance premium,wherein the machine learning model controls for consideration of one ormore undesired factors which might otherwise be considered by themachine learning model, may broadly comprise the following. The machinelearning model may be trained to probabilistically correlate an aspectof appearance with a personal and/or health-related characteristic byproviding machine learning model with a training data set of images ofindividuals having known personal or health-related characteristics,including the undesired factors. The undesired factors and one or morerelevant interaction terms between the undesired factors may beidentified. An image of the insurance applicant may be received via acommunication element. The machine learning model may analyze the imageof the insurance applicant to probabilistically determine the personaland/or health-related characteristics for the insurance applicant,wherein such analysis excludes the identified undesired factors. Themachine learning model may then suggest the appropriate insurancepremium based at least in part on the probabilistically determinedpersonal and/or health-related characteristic but not on the undesiredfactors.

Various implementations of these aspects may include any one or more ofthe following additional features. Identifying the undesired factors andrelevant interaction terms may include training a second machinelearning model using a second training data set that contains only theundesired factors and the relevant interaction terms. Further, causingthe machine learning model to not consider the identified undesiredfactors when analyzing the new data may include combining the machinelearning model and the second machine learning model to eliminate a biascreated by the undesired factors from the machine learning model'sconsideration prior to employing the machine learning model to analyzethe new data. Alternatively or additionally, identifying the undesiredfactors and relevant interaction terms may include training the machinelearning model to identify the undesired factors and the one or morerelevant interaction terms. Further, causing the machine learning modelto not consider the identified undesired factors when analyzing the newdata may include instructing the machine learning model to not considerthe identified undesired factors while analyzing the new data. Themachine learning model may be a neural network. The second machinelearning model may be a linear model. The machine learning model may betrained to analyze the new data as part of an underwriting process todetermine an appropriate insurance premium, and the new data may includeimages of a person applying for life insurance or health insurance orimages of a piece of property for which a person is applying forproperty insurance. The machine learning model may be further trained toanalyze the new data as part of the underwriting process to determineone or more appropriate terms of coverage.

Advantages of these and other embodiments will become more apparent tothose skilled in the art from the following description of the exemplaryembodiments which have been shown and described by way of illustration.As will be realized, the present embodiments described herein may becapable of other and different embodiments, and their details arecapable of modification in various respects. Accordingly, the drawingsand description are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of systems andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals. The present embodiments are notlimited to the precise arrangements and instrumentalities shown in theFigures.

FIG. 1 is a high level flowchart of a first exemplary embodiment of anaspect of the present technology involving training and using a machinelearning model;

FIG. 2 is a high level flowchart of a second exemplary embodiment of anaspect of the present technology involving training and using a machinelearning model;

FIG. 3 is a high level flowchart of an exemplary computer-implementedmethod of evaluating a life or health insurance applicant involving themachine learning models of FIG. 1 or 2;

FIG. 4 is a diagram of an exemplary system constructed in accordancewith embodiments of the present technology;

FIG. 5 is a flowchart of an exemplary computer-implemented methodpracticed in accordance with embodiments of the present technology; and

FIG. 6 is a flowchart of an exemplary computer-implemented method ofproviding life or health insurance quotes based upon, at least in part,video, image, or audio data samples received via wireless communicationor data transmission from an applicant's mobile device.

The Figures depict exemplary embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

The present embodiments may relate to, inter cilia, training and usingmachine learning models that control for consideration of one or moreundesired factors which might otherwise be considered by the machinelearning model when analyzing new data. For example, one embodiment ofthe present invention may be configured for training and using a neuralnetwork that controls for consideration of one or more undesired factorswhich might otherwise be considered by the neural network when analyzingnew data as part of an underwriting process to determine an appropriateinsurance premium and/or other terms of coverage.

Machine learning models may be trained to analyze information forparticular purposes, such as insurance underwriting, involvingidentifying correlations and making predictions. During training, themodels may learn to include undesired factors, especially if such biasesare present in the training data sets. In particular, while trainingwith structured data involves limiting the data that a model considers,training with unstructured data allows the model to consider allavailable data, including background information and undesired factors.For example, a neural network trained with unstructured data includingpeople's appearances to make correlations and predictions about thosepeople may consider such undesired factors as age, sex, ethnicity,and/or race in its subsequent analyses of new data.

The present technology concerns a method of training and using a machinelearning model that filters or otherwise controls for bias based uponthese undesired factors, including controlling for such bias in modelshaving unknown levels of bias. Broadly, this may be accomplished invarious ways. In a first exemplary embodiment, the one or more undesiredfactors may be eliminated from the neural network's consideration priorto it performing subsequent analyses of new data. In an exemplary secondembodiment, the neural network may itself be trained to identify the oneor more undesired factors and then ignore them while performingsubsequent analyses of new data.

In the first embodiment, a neural network may be trained on data thatcontains one or more undesired factors, a linear model may then betrained only on the undesired factors and the one or more relevantinteraction terms between the one or more undesired factors, and thenboth models may be combined to control for the bias created by theundesired factors. Referring to FIG. 1, the neural network may betrained using the training data set, as shown in 10. The trainingdataset may include images, sounds, or other data containing informationabout example subjects. For example, the information may concern one ormore characteristics of an individual or a property for which aninsurance (e.g., life, health, or property insurance) is sought. Basedupon correlations discovered through such training, the neural networkmay learn to analyze new subjects. However, the neural network may learnto include the one or more undesired factors in its analysis if suchfactors are present in the training data set. The output of this modelmay be referred to as “Y_Initial”.

A linear model may be built on the one or more factors consideredundesirable in the data and the relevant interaction terms between thosevariables, as shown in 12. This second model may have terms for all ofthe one or more undesired factors (e.g., B1, B2, . . . , Bn).

A new model may then be created which combines the trained neuralnetwork and the linear model, and which thereby controls for theundesired factors so that they are not considered when analyzing newdata as part of the underwriting process to determine the appropriateinsurance premium and/or other terms of coverage, as shown in 14. Thenew model may be referred to as “Y_Final”, whereinY_Final=Y_Initial+B1*X1+B2*X2+ . . . +Bn*Xn. X1, X2, . . . , Xn mayrepresent relative portions of the population containing the bias.Y_Final may then be known to not depend on any bias the neural networkmay have initially learned.

In the second embodiment, referring to FIG. 2, the neural network mayagain be trained on data that contains one or more undesired factors, asshown in 20. The same neural network may be trained to identify theundesired factors and the one or more relevant interaction terms betweenthe one or more undesired factors, as shown in 22. The neural networkmay then be instructed to not consider the identified undesired factorswhile analyzing new data as part of the underwriting process todetermine the appropriate insurance premium and/or other terms ofcoverage, as shown in 24.

In an exemplary application, the machine learning model may be a neuralnetwork trained on a set of training images to evaluate an insuranceapplicant based upon an image of the insurance applicant as part of anunderwriting process to determine an appropriate life or healthinsurance premium and/or other terms of coverage. The model may betrained to probabilistically correlate an aspect of the applicant'sappearance with a personal and/or health-related characteristic. Thetrained model may receive the image (e.g., a “selfie”) of the insuranceapplicant, analyze the image while excluding the identified undesiredfactors in accordance with the first or second embodiments discussedabove, and suggest or recommend the appropriate insurance premium and/orother terms of coverage based only on the remaining desired factors.

In more detail, referring to FIG. 3, once the neural network is trainedin accordance with the first or second embodiments discussed above andshown in FIGS. 1 and 2, as shown in 30, a processing element employingthe neural network may receive a still and/or moving (i.e., video) imageand/or voice recording of an insurance applicant, as shown in 32. Theprocessing element employing the neural network may then extractinformation to complete the underwriting process, as shown in 34, suchas verifying information provided by the applicant and/or answeringunderwriting questions, and/or may substantially automate aspects of theunderwriting process by directly predicting and suggesting theappropriate insurance premium and/or other terms of coverage, as shownin 36. The applicant may then quickly be provided with a rate quote, asshown in 38.

The neural network may be a convolutional neural network (CNN) and/or adeep learning neural network, A CNN is a type of feed-forward neuralnetwork often used in facial recognition systems, in which individualneurons may be tiled so as to respond to overlapping regions in thevisual field. A CNN may include multiple layers of small neuroncollections which examine small portions of an input image, calledreceptive fields. The results of these collections may be tiled so thatthey overlap to better represent the original image, and this may berepeated for each layer. Deep learning involves algorithms that attemptto model high-level abstractions in data by using model architectures,with complex structures or otherwise, composed of multiple non-lineartransformations. An image may be represented in various ways, such as avector of intensity values per pixel, a set of edges, or regions ofparticular shape. Certain representations may better facilitate learninghow to identify personal and health-related information from examples.

The large sample of still and/or moving (e.g., video images and/or voicerecordings used to train the neural network may be, for example,provided by volunteers, existing policy holders, or taken from socialmedia. The still and/or moving (e.g., video) image and/or voicerecording received from the applicant may be analog or digital andotherwise non-diagnostic and conventional in nature, such as an ordinary“selfie” taken by the insurance applicant or him- or herself. The videosmay include audio of the applicants' voices, and the neural network'straining and analysis may include similarly seeking relevantcharacteristics or patterns in voices. The neural network's analyses ofimages may be probabilistic, such that the resulting data may beassociated with varying degrees of certainty.

Thus, exemplary embodiments and applications may involveprobabilistically evaluating applicants for life and/or other insuranceand determining appropriate premiums and/or other terms of coveragebased upon analyses of still and/or moving images, and/or voicerecordings of the applicants and without requiring conventional medicalexaminations and while excluding undesired factors from the analyses.For descriptive purposes, exemplary applications of the technology aredescribed herein in detail with regard to facilitating underwriting oflife and/or health insurance, but it will be appreciated that thetechnology is similarly applicable to underwriting other forms ofinsurance, such as property insurance.

I. Exemplary Computer System

Referring to FIG. 4, an exemplary computer system 40 is shown configuredfor evaluating an insurance applicant as part of an underwriting processto determine one or more appropriate terms of life or other insurancecoverage, which may include appropriate premiums or discounts. Thesystem 40 may broadly comprise a memory element 42 configured to storeinformation, such as the database of training images and/or voicerecordings; a communication element 44 configured to receive andtransmit signals via a network 46, including receiving the applicant'simage and/or voice recording; and/or a processing element 48 employing aneural network 54 trained and configured to analyze the applicant'simage and/or voice recording.

More specifically, the memory element 42 may generally allow for storinginformation, such as the database of still and/or moving (e.g., video)images and/or voice recordings used to train the processing element 48,and still and/or moving (e.g., video) images and/or voice recordingsreceived from applicants. The memory element 42 may include data storagecomponents such as read-only memory (ROM), programmable ROM, erasableprogrammable ROM, random-access memory (RAM) such as static RAM (SRAM)or dynamic RAM (DRAM), cache memory, hard disks, floppy disks, opticaldisks, flash memory, thumb drives, USB ports, or the like, orcombinations thereof. The memory element 42 may include, or mayconstitute, a “computer-readable medium.” The memory element 42 mayfurther store instructions, code, code segments, software, firmware,programs, applications, apps, services, daemons, or the like that areexecuted by the processing element 48. The memory element 42 may alsostore additional settings, data, documents, sound files, photographs,movies, images, databases, and the like. The memory element 42 may beelectronically coupled or otherwise in electronic communication with thecommunication element 44 and the processing element 48.

The communication element 44 may generally allow for communication withremote systems or devices, including a system or device 52, such as asmartphone or other mobile communication device, configured to capturethe still and/or moving (e.g., video) image and/or voice recording ofthe applicant. The communication element 44 may include signal or datatransmitting and receiving circuits, such as antennas, amplifiers,filters, mixers, oscillators, digital signal processors (DSPs), and thelike. The communication element 44 may establish communicationwirelessly by utilizing radio-frequency (RF) signals (and/or one or moreradio links) and/or data that comply with communication standards suchas cellular 2G, 3G, or 4G, IEEE 802.11 standard (such as WiFi), IEEE802.16 standard (such as WiMAX), Bluetooth™, or combinations thereof.The communication element 44 may be electronically coupled or otherwisein electronic communication with the memory element 42 and theprocessing element 48.

The network 46 may be embodied by a local, metro, or wide area network(LAN, MAN, or WAN) and may be formed using a plurality of knownarchitectures and topologies. In some embodiments, a portion of thenetwork 46 may be formed by at least a portion of the Internet, bycommunication lines that are leased from other entities, or bycombinations thereof. The network 46 may be implemented within a smallarea such as city or across a larger area such as a region or country.

The processing element 48 may employ the neural network 54 trained inaccordance with the first or second embodiments discussed above andshown in FIGS. 1 and 2 to probabilistically correlate one or moreaspects of appearance and/or voice with one or more personal orhealth-related characteristics by being provided with the database ofstill and/or moving (e.g., video) images and/or voice recordings storedin the memory element 42 of individuals having known personal orhealth-related characteristics. The neural network 54 may be a CNN or adeep learning neural network. The processing element 48 employing theneural network 54 may be configured to analyze the still and/or movingimage and/or voice recording of the insurance applicant received via thecommunication element 44 to probabilistically determine the personal orhealth-related characteristic for the insurance applicant to facilitatethe completion of the underwriting process and/or to suggest one or moreappropriate terms of insurance coverage, such as an appropriate premiumor discount, based at least in part on the probabilistically determinedpersonal or health-related characteristic but without considering anyundesired factors.

The processing element 48 may include one or more processors,microprocessors, microcontrollers, DSPs, field-programmable gate arrays(FPGAs), analog and/or digital application-specific integrated circuits(ASICs), or the like, or combinations thereof. The processing element 48may generally execute, process, or run instructions, code, codesegments, software, firmware, programs, applications, apps, processes,services, daemons, or the like. The processing element 48 may alsoinclude hardware components such as finite-state machines, sequentialand combinational logic, and other electronic circuits that may performthe functions necessary for the operation of embodiments of the currentinventive concept. The processing element 48 may be in electroniccommunication with the memory element 42 and the communication element44. For example, the processing element 48 may communicate with theseand possibly other electronic components through serial or parallellinks that include address busses, data busses, control lines, and thelike.

The system 40 may include more, fewer, or alternative components and/orperform more, fewer, or alternative actions, including those discussedelsewhere herein, and particularly those discussed in the followingsection describing the computer-implemented method.

II. Exemplary Computer-Implemented Method

Referring to FIG. 5, an exemplary computer-implemented method 100 isshown for evaluating an insurance applicant as part of an underwritingprocess to determine one or more appropriate terms of life or otherinsurance coverage, which may include appropriate premiums or discounts,such as discounts for risk averse individuals or those living a healthylife style. Broadly, the computer-implemented method may comprise thefollowing actions. The processing element 44 employing the neuralnetwork 54 may be trained in accordance with the first or secondembodiment discussed above and shown in FIGS. 1 and 2 toprobabilistically correlate a one or more aspects of appearance and/orvoice with a personal or health-related characteristic by providing theneural network 54 with the database stored on the memory element 42 ofstill and/or moving (e.g., video) images and/or voice recordings ofindividuals having known personal or health-related characteristics, asshown in 102. The communication element 44 may receive the still and/ormoving image and/or voice recording of the insurance applicant, as shownin 104. The still and/or moving (e.g., video) image and/or voicerecording received from the applicant may be analog or digital andotherwise non-diagnostic and conventional in nature, such as an ordinaryselfie taken by the insurance applicant or him- or herself. The trainedneural network 54 employed by the processing element 48 may analyze theimage of the insurance applicant to probabilistically determine thepersonal or health-related characteristic for the insurance applicant,as shown in 106. The processing element 48 employing the neural network54 may suggest or recommend the appropriate term of insurance coverage,such as an appropriate premium, based at least in part on theprobabilistically determined personal or health-related characteristicbut without considering any undesired factors, as shown in 108.

The computer-implemented method may include more, fewer, or alternativeactions, including those discussed elsewhere herein.

III. Exemplary Computer-Readable Medium

Referring again to FIGS. 4 and 5, an exemplary non-transitorycomputer-readable medium with one or more executable programs storedthereon may be configured for evaluating an insurance applicant as partof an underwriting process to determine one or more appropriate terms oflife or other insurance coverage, which may include appropriatepremiums. Broadly, the one or more programs may instruct thecommunication element 44, processing element 48, neural network 54,and/or other components of the system 40 to perform the followingactions. The neural network 54 may be trained in accordance with thefirst or second embodiment discussed above and shown in FIGS. 1 and 2 toprobabilistically correlate a one or more aspects of appearance and/orvoice with a personal or health-related characteristic by providing theprocessing element 48 employing the neural network 54 with the databasestored on the memory element 42 of still and/or moving (e.g., video)images, and/or voice recordings of individuals having known personal orhealth-related characteristics, as shown in 102. The communicationelement 44 may be instructed to receive the still and/or moving image,and/or voice recording of the insurance applicant, as shown in 104. Thestill and/or moving image, and/or voice recording received from theapplicant may be analog or digital and otherwise non-diagnostic andconventional in nature, such as an ordinary selfie taken by theinsurance applicant or him- or herself. The trained neural network 54employed by processing element 48 may be instructed to analyze the imageof the insurance applicant to probabilistically determine the personalor health-related characteristic for the insurance applicant, as shownin 106. The processing element 48 employing the trained neural network54 may be instructed to suggest the appropriate term of insurancecoverage, such as an appropriate premium, based at least in part on theprobabilistically determined personal and/or health-relatedcharacteristic but without considering any undesired factors, as shownin 108.

The one or more executable programs stored on the non-transitorycomputer-readable medium may instruct the system 40 to perform more,fewer, or alternative actions, including those discussed elsewhereherein, and particularly those discussed in the section describing thecomputer-implemented method.

IV. Exemplary Video Functionality

In one aspect, a video magnification system may use a short video of anapplicant and extract the necessary health and personal data without theneed for fluid samples or medical review. For example, the video may beused to calculate the applicant's pulse, and could evolve to detectmedications or drug use through eye movements, and lead to otherinformation such as glucose levels and other measurements normallyattained through bodily fluid analysis. The results may be used toeither answer underwriting questions or automate the underwritingprocess by predicting the appropriate premium directly. Also, the use ofvideo magnification data may help prevent fraud (or buildup) by removingapplicants' ability to enter fraudulent information and ensuring anapplicant's identity.

In one embodiment, an online life or health insurance applicant may befilling out a virtual insurance application online, such as via theirmobile device or another computing device. The virtual insuranceapplication may ask the applicant to submit a short video or images ofthemselves, such as taken via their mobile device, for use withgenerating or adjusting an insurance quote, policy, premium, or discount(such as a life or health insurance application). The applicant maytransmit the short video or images of themselves from their mobiledevice to an insurance provider remote server via wireless communicationor data transmission over one or more radio links, or otherwiseelectronically attach the short video or images to the virtual insuranceapplication. Then with the customer's permission or affirmative consent,the insurance provider remote server or another processor may analyzethe short video or images, such as via video magnification or otherdigital image techniques, to identify risk, or lack thereof, associatedwith the applicant, or otherwise determine certain healthcharacteristics of the applicant. For instance, pulse, heart rate,medication or drug use, cigarette or alcohol use, glucose levels,cholesterol level, age, weight, an amount of exercise, sex, etc. may bedetermined from video or image analysis (such as be noticing pulsemovement or eye movement). As an example, cigarette use may bedetermined from image analysis of a person's teeth or gums, cholesterollevel may be determined from image analysis of a person's eyes, pulse orheart rate may be determined from image analysis of a person's neck orveins.

Based upon the risk, or lack thereof identified, or healthcharacteristics determined (such as pulse or glucose levels), theinsurance provider may estimate an insurance premium or discount for theapplicant, and transmit the insurance premium or discount to theapplicant's mobile device, via wireless communication or datatransmission over one or more radio links, for the applicant's review,approval, or modification. As a result, an online customer shoppingexperience for life or health insurance may be enhanced, and the needfor invasive procedures, such as giving blood and/or visiting a medicallab to have blood work performed, may be reduced.

V. Exemplary Audio Functionality

In another aspect, audio analysis techniques may use an audio recordingof an applicant's voice and extract the necessary health and personaldata without the need for fluid samples or medical review. The voiceanalysis system may learn to identify patterns and characteristics invoice recordings that are indicative of the presence of certain diseasesor medications, or be able to detect other characteristics of anapplicant, such as tobacco use. The results may be used to either answerunderwriting questions or automate the underwriting process bypredicting the appropriate premium directly. Further, the use of theaudio analysis system may help prevent fraud (or buildup) by removingapplicants' ability to enter fraudulent information and ensuring anapplicant's identity.

In one embodiment, an online life or health insurance applicant may befilling out a virtual insurance application online, such as via theirmobile device or another computing device. The virtual insuranceapplication may ask the applicant to submit a short audio of themselves,such as recorded via their mobile device (e.g., voice recorded within avideo), for use with generating or adjusting an insurance quote, policy,premium, or discount (such as a life or health insurance application).The applicant may transmit the short audio recording of themselves fromtheir mobile device to an insurance provider remote server, or otherwiseelectronically attach the short audio recording to the virtual insuranceapplication.

Then with the customer's permission or affirmative consent, theinsurance provider remote server or another processor may analyze theshort audio recording, such as via audio analysis or other digital audioprocessing techniques, to identify risk, or lack thereof, associatedwith the applicant, or otherwise determine certain healthcharacteristics of the applicant. For instance, certain diseases ormedication use, as well as cigarette use, age, weight, sex may bedetermined or estimated from audio analysis. Based upon the risk, orlack thereof identified, or health characteristics determined (such aslack of smoking), the insurance provider may estimate an insurancepremium or discount for the applicant, and transmit the insurancepremium or discount to the applicant's mobile device, via wirelesscommunication or data transmission, for the applicant's review,approval, or modification.

VI. Exemplary Computer-Implemented Methods

FIG. 6 depicts an exemplary computer-implemented method 400 of providinglife or health insurance quotes based upon, at least in part, video,image, or audio data samples received via wireless communication or datatransmission from an applicant's mobile device. The method 400 mayinclude with an individuals' permission, gathering video, images, oraudio of those with known health conditions or risk levels, or lackthereof 402. For instance, images, and/or audio of those with certaindiseases, associated with certain medication or drug use, associatedwith cigarette or alcohol usage, of a certain age or weight, of aspecific cholesterol or glucose level, or having other specific healthconditions or risk levels, or a lack of a health condition or ailment,or having a low risk level.

The method 400 may include analyzing the collected video, image(s), oraudio to build a database, table or other data structure correspondingto the known health conditions 404, For instance, a two column datastructure could include exemplary video, image(s), or audio (stored orlinked to by a pointer in column 1) that is associated with a certainhealth condition or risk level (that is stored in column 2 of the datastructure or table). Additionally or alternatively, instead of buildinga database, a neural network may be trained to identify certain healthconditions or risk levels from processor analysis of video, images, oraudio of individuals having known health conditions or ailments, such asdescribed elsewhere herein.

The method 400 may include asking an insurance applicant to submit avideo, image, or audio sample via their mobile device for a virtual lifeor health insurance application 406. For instance, after a database orneural network is trained to identify health conditions or health riskfrom video, image, or audio analysis, an online or virtual insuranceapplication form may include functionality that allows an application toattach a video, image, or audio sample to a virtual application usingtheir mobile device. The virtual application may give an applicant theoption submitting video, image, or audio data of themselves, and ask forconsent for an insurance provider to analyze the data sample submitted.In return, the applicant may not be required to submit to invasiveprocedures, such as drawing blood (or even a visit to nurse or doctor),and risk averse applicants may be entitled to a discount, such as thosethat don't smoke or drink heavily, or that exercise or otherwise live ahealth conscious life.

The method 400 may include analyzing the video, image, or audio datasample received from the application with the applicant's permission oraffirmative consent 408. For instance, the applicant's mobile device maytransmit the video, image, or audio data sample to an insurance providerremote server, and the data sample may be analyzed via comparison ofvideo, image, or audio data stored in the database of known healthconditions, or otherwise used to train a neural network.

The method 400 may include, from comparison of the online applicants'video, image, or audio data sample with video, image, audio data storedin the database (or used to train the neural network) to determinehealth conditions or risk level for the applicant 410. From analysis ofthe data sample various health conditions may be determined via aprocessor, such as sex, weight, body mass index, cholesterol, amount ofexercise, cigarette or alcohol use, medication or drug use, certaindiseases or ailments, glucose levels, and other health conditions orrisks, including those discussed elsewhere herein.

The method 400 may include generating or adjusting (such as at aninsurance provider remote server) an insurance policy, premium, ordiscount based upon the health conditions or risk levels determined 412.For instance, risk averse applicants may have their lack of riskverified via their video, image, or data samples, such as lack ofsmoking or drug use, or an appropriate amount of exercise. As a result,those applicants may receive an online insurance discount on health orlife insurance, for instance.

The method 400 may include transmitting an insurance quote, premium, ordiscount from an insurance provider remote server to the applicant'smobile device for their review and approval 414. For instance, a quotefor insurance may be transmitted to an applicant's mobile or othercomputing device for their review and approval via wirelesscommunication and/or data transmission to enhance on online customerexperience associated with shopping for and/or receiving binding life orhealth insurance.

In another aspect, a computer-implemented method for evaluating aninsurance applicant as part of an underwriting process to determine alife or health insurance policy, premium, or discount may be provided.The computer-implemented method may include (1) training a processingelement to probabilistically correlate an aspect of appearance with apersonal and/or health-related characteristic by providing theprocessing element with a database of images of individuals having knownpersonal or health-related characteristics; (2) receiving with acommunication element an image of the insurance applicant; (3) analyzingthe image of the insurance applicant with the trained processing elementto probabilistically determine the personal and/or health-relatedcharacteristic for the insurance applicant; and/or (4) generating oradjusting via the processing element a life or health insurance policy,premium, or discount based at least in part on the probabilisticallydetermined personal and/or health-related characteristic.

The personal and/or health-related characteristic may be a pulse orheart rate. The personal and/or health-related characteristic mayindicate, or be associated with, smoking, a lack of smoking, or anamount or frequency of smoking. The personal and/or health-relatedcharacteristic may indicate, or be associated with, drug or alcohol use,a lack of drug or alcohol use, or an amount or frequency of drug oralcohol use.

In another aspect, a computer-implemented method for evaluating aninsurance applicant as part of an underwriting process to determine anappropriate life insurance premium may be provided. Thecomputer-implemented method may include (1) training a processingelement having a neural network to probabilistically or otherwisecorrelate one or more aspects of appearance with a personal and/orhealth-related characteristic by providing the processing element with adatabase of otherwise non-diagnostic conventional images of individualshaving known personal and/or health-related characteristics; (2)receiving with a communication element an otherwise non-diagnosticconventional image of the insurance applicant; (3) analyzing with thetrained processing element the otherwise non-diagnostic conventionalimage of the insurance applicant to probabilistically or otherwisedetermine the personal and/or health-related characteristic for theinsurance applicant; (4) using, by the processing element, theprobabilistically, otherwise determined personal and/or health-relatedcharacteristic to verify information provided by the insuranceapplicant; and/or (5) automatically determining or adjusting with theprocessing element a life or health insurance premium or discount basedat least in part on the probabilistically or otherwise determinedpersonal and/or health-related characteristic.

In another aspect, a computer-implemented method for evaluatingapplicant provided images to adjust or generate a life or healthinsurance policy, premium, or discount may be provided. Thecomputer-implemented method may include (1) receiving, via one or moreprocessors, image data from an applicant's mobile device; (2) comparing;via the one or more processors, the image data with images stored in adatabase that have corresponding pre-determined health conditions orrisk levels; (3) identifying, via the one or more processors, a healthcondition or risk level for the applicant based upon the comparison ofthe applicant's image data with the images stored in the database;and/or (4) automatically determining or adjusting, via the one or moreprocessors, a life or health insurance premium or discount based atleast in part on the health condition or risk level for the applicantthat is determined from their image data to facilitate providing moreaccurate or appropriate insurance premiums in view of risk, or lackthereof, to insurance customers. The health condition or risk level maybe associated with or determined based upon whether or not the applicantsmokes, or an amount that the applicant smokes (determined fromprocessor analysis of the applicant's image data). The health conditionor risk level may be associated with, or determined based upon, whetheror not the applicant uses drugs, or pulse or heart rate of the applicantdetermined from processor analysis of the applicant's image data.

In another aspect, a computer-implemented method for evaluatingapplicant provided images to adjust or generate a life or healthinsurance policy, premium, or discount may be provided. Thecomputer-implemented method may include (1) receiving, via one or moreprocessors (and/or associated transceivers, such as via wirelesscommunication or data transmission over one or more radio links), anindication from an applicant's mobile device that the applicant isinterested in applying for insurance and/or receiving an onlineinsurance application from the applicant's mobile device; (2)transmitting, via the one or more processors (and/or associatedtransceivers), a request for the applicant to transmit image data of theapplicant from their mobile device for use with determining an accurateinsurance premium or discount; (3) receiving, via one or more processors(and/or associated transceivers), the image data from the applicant'smobile device; (4) identifying, via the one or more processors, a healthcondition or risk level for the applicant (and/or a personal and/orhealth-related characteristic) based upon the computer analysis of theapplicant's image data; (5) automatically determining or adjusting, viathe one or more processors, a life or health insurance premium ordiscount based at least in part on the health condition or risk levelfor the applicant that is determined from their image data; and/or (6)transmitting, via the one or more processors (and/or an associatedtransceiver), the life or health insurance premium or discount to theapplicant's mobile device for their review and/or approval to facilitateproviding more accurate insurance premiums to insurance customers andenhancing the online customer experience. The health condition or risklevel (and/or personal and/or health-related characteristic) determinedfrom computer analysis of the applicant's image data may be a pulse orheart rate. The health condition or risk level (and/or personal and/orhealth-related characteristic) determined from computer analysis of theapplicant's image data may indicate, or may be associated with, smoking,a lack of smoking, or an amount or frequency of smoking. Additionally oralternatively, the health condition or risk level (and/or personaland/or health-related characteristic) determined from computer analysisof the applicant's image data may indicate; or be associated with, drugor alcohol use, a lack of drug or alcohol use, or an amount or frequencyof drug or alcohol use.

In another aspect, a computer-implemented method for evaluatingapplicant provided audio to adjust or generate a life or healthinsurance policy, premium, or discount may be provided. Thecomputer-implemented method may include (1) receiving, via one or moreprocessors; an indication from an applicant's mobile device that theapplicant is interested in applying for insurance and/or receiving anonline insurance application from the applicant's mobile device; (2)transmitting, via the one or more processors, a request for theapplicant to transmit audio data of the applicant from their mobiledevice for use with determining an accurate insurance premium ordiscount; (3) receiving, via one or more processors; the audio data fromthe applicant's mobile device; (4) identifying, via the one or moreprocessors, a health condition or risk level for the applicant (and/or apersonal and/or health-related characteristic) based upon the computeranalysis of the applicant's audio data; (5) automatically determining oradjusting, via the one or more processors, a life or health insurancepremium or discount based at least in part on the health condition orrisk level for the applicant that is determined from their audio data;and/or (6) transmitting, via the one or more processors, the life orhealth insurance premium or discount to the applicant's mobile devicefor their review and/or approval to facilitate providing more accurateinsurance premiums to insurance customers and enhancing the onlinecustomer experience.

The foregoing methods may include additional, less, or alternatefunctionality, including that discussed elsewhere herein. Further, theforegoing methods may be implemented via one or more local or remoteprocessors and/or transceivers, and/or via computer-executableinstructions stored on non-transitory computer-readable medium or media.

VII. Exemplary Computer Systems

In one aspect; a computer system for evaluating an insurance applicantas part of an underwriting process to determine one or more appropriateterms of insurance coverage may be provided. The system may include acommunication element configured to receive an image of the insuranceapplicant; and a processing element trained to probabilisticallycorrelate an aspect of appearance with a personal and/or health-relatedcharacteristic by being provided with a database of images ofindividuals having known personal and/or health-related characteristics,and configured to analyze the image of the insurance applicant toprobabilistically determine the personal and/or health-relatedcharacteristic for the insurance applicant, and to generate a proposedlife or health insurance policy, premium, or discount for the insuranceapplicant based upon the personal and/or health-related characteristicprobabilistically determined from the image.

The communication element may be further configured to receive an voicerecording of the insurance applicant; and the processing elementis—trained to probabilistically correlate an aspect of voice with thepersonal and/or health-related characteristic by being provided with adatabase of voice recordings of individuals having the known personaland/or health related characteristics, and configured to analyze thevoice recording of the insurance applicant to probabilisticallydetermine the personal and/or health-related characteristic for theinsurance applicant, and generate or adjust a proposed life or healthinsurance policy, premium, or discount based upon at least in part theprobabilistically determined personal and/or health-relatedcharacteristic.

The personal and/or health-related characteristic may be a pulse orheart rate. The personal and/or health-related characteristic mayindicate, or be associated with, smoking, a lack of smoking, or anamount or frequency of smoking. The personal and/or health-relatedcharacteristic may indicate, or be associated with, alcohol or drug use,a lack of alcohol or drug use, or an amount or frequency of alcohol ordrug use.

In another aspect, a computer system for evaluating an insuranceapplicant as part of an underwriting process to determine a life orhealth insurance policy, premium, or discount may be provided. Thecomputer system may include one or more processors configured to: (1)train a processing element to probabilistically correlate an aspect ofappearance with a personal and/or health-related characteristic byproviding the processing element with a database of images ofindividuals having known personal or health-related characteristics; (2)receive with a communication element an image of the insuranceapplicant; (3) analyze the image of the insurance applicant with thetrained processing element to probabilistically determine the personaland/or health-related characteristic for the insurance applicant; and/or(4) generate or adjust via the processing element a life or healthinsurance policy, premium, or discount based at least in part on theprobabilistically determined personal and/or health-relatedcharacteristic.

In another aspect, a computer system for evaluating an insuranceapplicant as part of an underwriting process to determine an appropriatelife insurance premium may be provided. The computer system may includeone or more processors configured to: (1) train a processing elementhaving a neural network to probabilistically correlate one or moreaspects of appearance with a personal and/or health-relatedcharacteristic by providing the processing element with a database ofotherwise non-diagnostic conventional images of individuals having knownpersonal and/or health-related characteristics; (2) receive with acommunication element an otherwise non-diagnostic conventional image ofthe insurance applicant; (3) analyze with the trained processing elementthe otherwise non-diagnostic conventional image of the insuranceapplicant to probabilistically determine the personal and/orhealth-related characteristic for the insurance applicant; (4) use, byor via the processing element, the probabilistically determined personaland/or health-related characteristic to verify information provided bythe insurance applicant; and/or (5) automatically determine or adjustwith the processing element a life or health insurance premium ordiscount based at least in part on the probabilistically determinedpersonal and/or health-related characteristic.

In another aspect, a computer system for evaluating applicant providedimages to adjust or generate a life or health insurance policy, premium,or discount may be provided. The computer system may include one or moreprocessors and/or associated transceivers configured to: (1) receiveimage data from an applicant's mobile device, such as via wirelesscommunication or data transmission over one or more radio links; (2)compare the image data with images stored in a database that havecorresponding pre-determined health conditions or risk levels; (3)identify a health condition or risk level (such as low, medium, or highrisk) for the applicant based upon the comparison of the applicant'simage data with the images stored in the database; and/or (4)automatically determine or adjust a life or health insurance premium ordiscount based at least in part on the health condition or risk levelfor the applicant that is determined from their image data to facilitateproviding more accurate or appropriate insurance premiums in view ofrisk, or lack thereof, to insurance customers. The health condition orrisk level may be associated with or determined based upon whether ornot the applicant smokes, or an amount that the applicant smokes(determined from processor analysis of the applicant's image data). Thehealth condition or risk level may be associated with, or determinedbased upon, whether or not the applicant uses drugs, or pulse or heartrate of the applicant determined from processor analysis of theapplicant's image data.

In another aspect, a computer system for evaluating applicant providedimages to adjust or generate a life or health insurance policy, premium,or discount may be provided. The computer system may include one or moreprocessors and/or transceivers configured to: (1) receive an indication,via wireless communication or data transmission over one or more radiolinks, from an applicant's mobile device that the applicant isinterested in applying for insurance and/or receive an online or virtualinsurance application from the applicant's mobile device; (2) transmit,via wireless communication or data transmission, a request for theapplicant to transmit image data of the applicant from their mobiledevice for use with determining an accurate insurance premium ordiscount; (3) receive, via wireless communication or data transmissionover one or more radio links, the image data from the applicant's mobiledevice; (4) identify or determine a health condition or risk level forthe applicant (and/or a personal and/or health-related characteristic)based upon the computer analysis of the applicant's image data (with thecustomer's permission or affirmative consent); (5) automaticallydetermine or adjust a life or health insurance premium or discount basedat least in part on the health condition or risk level for the applicantthat is determined from their image data; and/or (6) transmit anestimated insurance premium or discount to the applicant's mobile deviceover one or more radio links for their review and approval to facilitateproviding more accurate insurance premiums to insurance customers andenhancing the online customer experience.

In another aspect, a computer system configured for evaluating applicantprovided audio to adjust or generate a life or health insurance policy,premium, or discount may be provided. The computer system may includeone or more processors and/or transceivers configured to: (1) receive anindication from an applicant's mobile device that the applicant isinterested in applying for insurance and/or receiving an onlineinsurance application from the applicant's mobile device; (2) transmit arequest for the applicant to transmit audio data of the applicant fromtheir mobile device for use with determining an accurate insurancepremium or discount; (3) receive the audio data from the applicant'smobile device; (4) identify or determine a health condition or risklevel for the applicant (and/or a personal and/or health-relatedcharacteristic) based upon the computer analysis of the applicant'saudio data (with the customer's permission or affirmative consent); (5)automatically determine or adjust a life or health insurance premium ordiscount based at least in part on the health condition or risk levelfor the applicant that is determined from their audio data; and/or (6)transmit the life or health insurance premium or discount to theapplicant's mobile device for their review and/or approval to facilitateproviding more accurate insurance premiums to insurance customers andenhancing the online customer experience.

The foregoing computer systems may be configured with additional; less,or alternate functionality, including that discussed elsewhere hereinand that described with respect to FIG. 4. The foregoing computersystems may include computer-executable instructions stored onnon-transitory computer-readable medium or media.

VIII. Additional Considerations

In this description, references to “one embodiment”, “an embodiment”, or“embodiments” mean that the feature or features being referred to areincluded in at least one embodiment of the technology. Separatereferences to “one embodiment”, “an embodiment”, or “embodiments” inthis description do not necessarily refer to the same embodiment and arealso not mutually exclusive unless so stated and/or except as will bereadily apparent to those skilled in the art from the description. Forexample, a feature, structure, act, etc. described in one embodiment mayalso be included in other embodiments, but is not necessarily included.Thus, the current technology may include a variety of combinationsand/or integrations of the embodiments described herein.

Although the present application sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof routines, subroutines, applications, or instructions. These mayconstitute either software (e.g., code embodied on a machine-readablemedium or in a transmission signal) or hardware. In hardware, theroutines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) ascomputer hardware that operates to perform certain operations asdescribed herein.

In various embodiments, computer hardware, such as a processing element,may be implemented as special purpose or as general purpose. Forexample, the processing element may comprise dedicated circuitry orlogic that is permanently configured, such as an application-specificintegrated circuit (ASIC), or indefinitely configured, such as an FPGA,to perform certain operations. The processing element may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement the processingelement as special purpose, in dedicated and permanently configuredcircuitry, or as general purpose (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “processing element” or equivalents should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. Consideringembodiments in which the processing element is temporarily configured(e.g., programmed), each of the processing elements need not beconfigured or instantiated at any one instance in time. For example,where the processing element comprises a general-purpose processorconfigured using software, the general-purpose processor may beconfigured as respective different processing elements at differenttimes. Software may accordingly configure the processing element toconstitute a particular hardware configuration at one instance of timeand to constitute a different hardware configuration at a differentinstance of time.

Computer hardware components, such as communication elements, memoryelements, processing elements, and the like, may provide information to,and receive information from, other computer hardware components.Accordingly, the described computer hardware components may be regardedas being communicatively coupled. Where multiple of such computerhardware components exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the computer hardware components. In embodimentsin which multiple computer hardware components are configured orinstantiated at different times, communications between such computerhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplecomputer hardware components have access. For example, one computerhardware component may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther computer hardware component may then, at a later time, accessthe memory device to retrieve and process the stored output. Computerhardware components may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processing elements thatare temporarily configured (e.g., by software) or permanently configuredto perform the relevant operations. Whether temporarily or permanentlyconfigured, such processing elements may constitute processingelement-implemented modules that operate to perform one or moreoperations or functions. The modules referred to herein may, in someexample embodiments, comprise processing element-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processing element-implemented. For example, at least some ofthe operations of a method may be performed by one or more processingelements or processing element-implemented hardware modules. Theperformance of certain of the operations may be distributed among theone or more processing elements, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processing elements may be located in a single location(e.g., within a home environment, an office environment or as a serverfarm), while in other embodiments the processing elements may bedistributed across a number of locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer with a processing element andother computer hardware components) that manipulates or transforms datarepresented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s).

Although the invention has been described with reference to theembodiments illustrated in the attached drawing figures, it is notedthat equivalents may be employed and substitutions made herein withoutdeparting from the scope of the invention as recited in the claims.

Having thus described various embodiments of the invention, what isclaimed as new and desired to be protected by Letters Patent includesthe following:

We claim:
 1. A computer-implemented method for training and using aneural network to evaluate an insurance applicant as part of anunderwriting process to determine an appropriate insurance premium,wherein the neural network controls for consideration of one or moreundesired factors which might otherwise be considered by the neuralnetwork, the computer-implemented method comprising, via one or moreprocessors: training the neural network to probabilistically correlatean aspect of appearance with a personal and/or health-relatedcharacteristic by providing the neural network with a training data setof images of individuals having known personal or health-relatedcharacteristics, including the one or more undesired factors;identifying the one or more undesired factors; receiving via acommunication element an image of the insurance applicant; analyzingwith the neural network the image of the insurance applicant toprobabilistically determine the personal and/or health-relatedcharacteristics for the insurance applicant, wherein such analysisexcludes the identified one or more undesired factors; and suggestingwith the neural network the appropriate insurance premium based at leastin part on the probabilistically determined personal and/orhealth-related characteristic but not on the one or more undesiredfactors to control for undesired prejudice or discrimination in neuralnetworks.
 2. The computer-implemented method as set forth in claim 1,wherein identifying the one or more undesired factors includes traininga second neural network using a second training data set that containsonly the one or more undesired factors and one or more relevantinteraction terms between the one or more undesired factors.
 3. Thecomputer-implemented method as set forth in claim 2, wherein causing theneural network to exclude the identified one or more undesired factorswhen analyzing the image includes combining the neural network and thesecond neural network to eliminate a bias created by the one or moreundesired factors from the neural network's consideration prior toemploying the neural network to analyze the image.
 4. Thecomputer-implemented method as set forth in claim 1, wherein causing theneural network to exclude the identified one or more undesired factorswhen analyzing the image includes training the neural network toidentify the one or more undesired factors and one or more relevantinteraction terms between the one or more undesired factors.
 5. Thecomputer-implemented method as set forth in claim 4, wherein causing theneural network to exclude the identified one or more undesired factorswhen analyzing the image includes instructing the neural network to notconsider the identified one or more undesired factors while analyzingthe image.
 6. The computer-implemented method as set forth in claim 1,wherein the image of the insurance applicant is a selfie image takenwith a smartphone and transmitted via a wireless communications network.7. A computer system configured to train and use a neural network toevaluate an insurance applicant as part of an underwriting process todetermine an appropriate insurance premium, wherein the neural networkcontrols for consideration of one or more undesired factors which mightotherwise be considered by the neural network, the computer systemcomprising one or more processors configured to: train the neuralnetwork to probabilistically correlate an aspect of appearance with apersonal and/or health-related characteristic by providing the neuralnetwork with a training data set of images of individuals having knownpersonal or health-related characteristics, including the one or moreundesired factors; identify the one or more undesired factors; receivevia a communication element an image of the insurance applicant; analyzewith the neural network the image of the insurance applicant toprobabilistically determine the personal and/or health-relatedcharacteristics for the insurance applicant, wherein such analysisexcludes the identified one or more undesired factors; and suggest orrecommend with the neural network the appropriate insurance premiumbased at least in part on the probabilistically determined personaland/or health-related characteristic but not on the one or moreundesired factors to control for undesired prejudice or discriminationin machine learning models.
 8. The computer system as set forth in claim7, wherein identifying the one or more undesired factors includes theone or more processors training a second neural network using a secondtraining data set that contains only the one or more undesired factorsand one or more relevant interaction terms between the one or moreundesired factors.
 9. The computer system as set forth in claim 8,wherein causing the neural network to exclude the identified one or moreundesired factors when analyzing the image includes the one or moreprocessors combining the neural network and the second neural network toeliminate a bias created by the one or more undesired factors from theneural network's consideration prior to employing the neural network toanalyze the image.
 10. The computer system as set forth in claim 7,wherein causing the neural network to exclude the identified one or moreundesired factors when analyzing the image includes the one or moreprocessors training the neural network to identify the one or moreundesired factors and one or more relevant interaction terms between theone or more undesired factors.
 11. The computer system as set forth inclaim 7, wherein causing the neural network to exclude the identifiedone or more undesired factors when analyzing the image includes the oneor more processors instructing the neural network to not consider theidentified one or more undesired factors while analyzing the image. 12.The computer system as set forth in claim 7, wherein the image of theinsurance applicant is a selfie image taken with a smartphone andtransmitted via a wireless communications network.
 13. Acomputer-implemented method for training and using a neural network toevaluate an insurance applicant as part of an underwriting process todetermine an appropriate insurance premium, wherein the neural networkcontrols for consideration of one or more undesired factors which mightotherwise be considered by the neural network, the computer-implementedmethod comprising, via one or more processors: training the neuralnetwork to probabilistically correlate an aspect of appearance with apersonal and/or health-related characteristic by providing the neuralnetwork with a training data set of images of individuals having knownpersonal or health-related characteristics, including the one or moreundesired factors; identifying the one or more undesired factors;receiving via a communication element a selfie image of the insuranceapplicant taken with a smartphone and transmitted via a wirelesscommunications network; analyzing with the neural network the selfieimage of the insurance applicant to probabilistically determine thepersonal and/or health-related characteristics for the insuranceapplicant, wherein such analysis excludes the identified one or moreundesired factors; and suggesting with the neural network theappropriate insurance premium based at least in part on theprobabilistically determined personal and/or health-relatedcharacteristic but not on the one or more undesired factors to controlfor undesired prejudice or discrimination in the neural network.
 14. Thecomputer-implemented method as set forth in claim 13, whereinidentifying the one or more undesired factors includes training a linearmachine learning model using a second training data set that containsonly the one or more undesired factors and one or more relevantinteraction terms between the one or more undesired factors.
 15. Thecomputer-implemented method as set forth in claim 13, wherein causingthe neural network to exclude the identified one or more undesiredfactors when analyzing the selfie image includes combining the neuralnetwork and the linear machine learning model to eliminate a biascreated by the one or more undesired factors from consideration by theneural network prior to employing the neural network to analyze theselfie image.
 16. The computer-implemented method as set forth in claim13, wherein causing the neural network to exclude the identified one ormore undesired factors when analyzing the selfie image includesinstructing the neural network to not consider the identified one ormore undesired factors while analyzing the selfie image.